Description
We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully--automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed.
The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC--IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC--IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system.
The download links provided below provide easy access to specific subsets of images from our study, which are described in much greater detail in our publication (https://doi.org/10.1016/j.media.2015.02.002).
Localtab Group |
---|
Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with
Collections Used in this Third Party AnalysisBelow is a list of the Collections used in these analyses: Data Type | Download | License |
---|
Corresponding Original CT Images from LIDC-IDRI containing the 66 testing nodules that are delineated by all four board certified radiologists (DICOM) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/19038755/LIDC-66-nodules.tcia?api=v2 |
---|
|
|
(Download requires the NBIA Data Retriever |
| Data Type | Download all or Query/Filter |
---|
Image Data (DICOM) | | Supplemental Data (DICOM) | | ) | | Corresponding Original CT Images from LIDC-IDRI containing the 77 LIDC testing nodules that are segmented by three or more radiologists (DICOM) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/19038755/LIDC-77-nodules.tcia?api=v2 |
---|
|
|
(Download requires the NBIA Data Retriever) | |
Click the Versions tab for more info about data releases. Please contact help@cancerimagingarchive.netPlease contact TCIA's Helpdesk with any questions regarding usage.
Localtab |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage Policy These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work: Tcia limited license policy |
---|
Info |
---|
| Temesguen Messay T, Russell C Hardie RC, and Timothy R Tuinstra TR. (2014). Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset (Pulmonary-Nodules-Segmentation). The Cancer Imaging Archive. http https://doi.org/10.7937/K9/TCIA.2014.V7CVH1JO |
Info |
---|
title | Publication Citation |
---|
| Messay T, Hardie RC, Tuinstra TR. (2015). Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Medical Image Analysis. Elsevier BV. https://doi.org/10.1016/j.media.2015.02.002 |
Info |
---|
| Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. The (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, . In Journal of Digital Imaging , Volume (Vol. 26, Number Issue 6, December, 2013, pp 1045-1057. (paper) |
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research: Info |
---|
title | Publication Citation |
---|
| Messay, T., Hardie, R. C., & Tuinstra, T. R. (2015, May). Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Medical Image Analysis. Elsevier BV. http1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1016/j.media.2015.02.0021007/s10278-013-9622-7 PMCID: PMC3824915 |
Other Publications Using This DataTCIA maintainsmaintains a list of publications that leverage TCIA our data. If you have a manuscript you'd like to add pleaseplease contact the TCIA's Helpdesk. - Gomes, J. H. O. (2017). Pulmonary nodule segmentation in computed tomography with deep learning. (M.S. Thesis). Instituto Universitário de Lisboa, Retrieved from http://hdl.handle.net/10071/15479
|
Localtab |
---|
| Version 1 (Current): 20162015/0802/0224
Data Type | Download all or Query/Filter |
---|
Image Data Images containing the 66 testing nodules that are delineated by all four board certified radiologists (DICOM) | | Supplemental Data | Images containing the 77 LIDC testing nodules that are segmented by three or more radiologists (DICOM) | |
|
|